In [4]:

import pandas as pd

In [18]:

btc = pd.read_csv('coin_bitcoin.csv')btc

Out[18]:

	SNo	Name	Symbol	Date	High	Low	Open	Close	Volume	Marketcap
0	1	Bitcoin	BTC	2013-04-29 23:59:59	147.488007	134.000000	134.444000	144.539993	0.000000e+00	1.603769e+09
1	2	Bitcoin	BTC	2013-04-30 23:59:59	146.929993	134.050003	144.000000	139.000000	0.000000e+00	1.542813e+09
2	3	Bitcoin	BTC	2013-05-01 23:59:59	139.889999	107.720001	139.000000	116.989998	0.000000e+00	1.298955e+09
3	4	Bitcoin	BTC	2013-05-02 23:59:59	125.599998	92.281898	116.379997	105.209999	0.000000e+00	1.168517e+09
4	5	Bitcoin	BTC	2013-05-03 23:59:59	108.127998	79.099998	106.250000	97.750000	0.000000e+00	1.085995e+09
...	...	...	...	...	...	...	...	...	...	...
2986	2987	Bitcoin	BTC	2021-07-02 23:59:59	33939.588699	32770.680780	33549.600177	33897.048590	3.872897e+10	6.354508e+11
2987	2988	Bitcoin	BTC	2021-07-03 23:59:59	34909.259899	33402.696536	33854.421362	34668.548402	2.438396e+10	6.499397e+11
2988	2989	Bitcoin	BTC	2021-07-04 23:59:59	35937.567147	34396.477458	34665.564866	35287.779766	2.492431e+10	6.615748e+11
2989	2990	Bitcoin	BTC	2021-07-05 23:59:59	35284.344430	33213.661034	35284.344430	33746.002456	2.672155e+10	6.326962e+11
2990	2991	Bitcoin	BTC	2021-07-06 23:59:59	35038.536363	33599.916169	33723.509655	34235.193451	2.650126e+10	6.418992e+11

2991 rows × 10 columns

In [4]:

btc.info()

RangeIndex: 2991 entries, 0 to 2990

Data columns (total 10 columns):

# Column Non-Null Count Dtype

--- ------ -------------- -----

0 SNo 2991 non-null int64

1 Name 2991 non-null object

2 Symbol 2991 non-null object

3 Date 2991 non-null object

4 High 2991 non-null float64

5 Low 2991 non-null float64

6 Open 2991 non-null float64

7 Close 2991 non-null float64

8 Volume 2991 non-null float64

9 Marketcap 2991 non-null float64

dtypes: float64(6), int64(1), object(3)

memory usage: 233.8+ KB

In [28]:

btc.set_index('High', inplace=False)

Out[28]:

	SNo	Name	Symbol	Low	Open	Close	Volume	Marketcap
High
147.488007	1	Bitcoin	BTC	134.000000	134.444000	144.539993	0.000000e+00	1.603769e+09
146.929993	2	Bitcoin	BTC	134.050003	144.000000	139.000000	0.000000e+00	1.542813e+09
139.889999	3	Bitcoin	BTC	107.720001	139.000000	116.989998	0.000000e+00	1.298955e+09
125.599998	4	Bitcoin	BTC	92.281898	116.379997	105.209999	0.000000e+00	1.168517e+09
108.127998	5	Bitcoin	BTC	79.099998	106.250000	97.750000	0.000000e+00	1.085995e+09
...	...	...	...	...	...	...	...	...
33939.588699	2987	Bitcoin	BTC	32770.680780	33549.600177	33897.048590	3.872897e+10	6.354508e+11
34909.259899	2988	Bitcoin	BTC	33402.696536	33854.421362	34668.548402	2.438396e+10	6.499397e+11
35937.567147	2989	Bitcoin	BTC	34396.477458	34665.564866	35287.779766	2.492431e+10	6.615748e+11
35284.344430	2990	Bitcoin	BTC	33213.661034	35284.344430	33746.002456	2.672155e+10	6.326962e+11
35038.536363	2991	Bitcoin	BTC	33599.916169	33723.509655	34235.193451	2.650126e+10	6.418992e+11

2991 rows × 8 columns

In [29]:

btc.Open

Out[29]:

Date

2013-04-29 23:59:59 134.444000

2013-04-30 23:59:59 144.000000

2013-05-01 23:59:59 139.000000

2013-05-02 23:59:59 116.379997

2013-05-03 23:59:59 106.250000

...

2021-07-02 23:59:59 33549.600177

2021-07-03 23:59:59 33854.421362

2021-07-04 23:59:59 34665.564866

2021-07-05 23:59:59 35284.344430

2021-07-06 23:59:59 33723.509655

Name: Open, Length: 2991, dtype: float64

In [30]:

btc.High.plot()

Out[30]:

<Axes: xlabel='Date'>

In [32]:

btc = pd.read_csv('coin_bitcoin.csv')btc.High

Out[32]:

0 147.488007

1 146.929993

2 139.889999

3 125.599998

4 108.127998

...

2986 33939.588699

2987 34909.259899

2988 35937.567147

2989 35284.344430

2990 35038.536363

Name: High, Length: 2991, dtype: float64

In [34]:

btc.High.plot()

Out[34]:

<Axes: >

In [13]:

countries = pd.read_csv('world-happiness-report-2021.csv')countries

Out[13]:

	Country name	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
0	Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
1	Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
2	Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
3	Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
4	Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
144	Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
145	Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
146	Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
147	Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
148	Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 20 columns

In [230]:

countries[['Healthy life expectancy']]

Out[230]:

	Healthy life expectancy
0	72.000
1	72.700
2	74.400
3	73.000
4	72.400
...	...
144	48.700
145	59.269
146	61.400
147	56.201
148	52.493

149 rows × 1 columns

In [15]:

countries.set_index('Country name')

Out[15]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 19 columns

In [24]:

countries['Healthy life expectancy'].head(25).plot(kind = 'barh', color = 'magenta')

Out[24]:

<Axes: >

In [28]:

countries = pd.read_csv('world-happiness-report-2021.csv', index_col = 'Country name')countries

Out[28]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 19 columns

In [32]:

countries = pd.read_csv('world-happiness-report-2021.csv')countries

Out[32]:

	Country name	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
0	Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
1	Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
2	Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
3	Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
4	Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
144	Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
145	Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
146	Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
147	Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
148	Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 20 columns

In [35]:

countries.sort_values('Healthy life expectancy')

Out[35]:

	Country name	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
127	Chad	Sub-Saharan Africa	4.355	0.094	4.540	4.171	7.364	0.619	48.478	0.579	0.041	0.807	2.43	0.255	0.353	0.000	0.240	0.215	0.084	3.209
144	Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
115	Nigeria	Sub-Saharan Africa	4.759	0.052	4.861	4.658	8.533	0.740	50.102	0.737	0.037	0.878	2.43	0.663	0.625	0.051	0.433	0.212	0.039	2.736
84	Ivory Coast	Sub-Saharan Africa	5.306	0.078	5.460	5.152	8.551	0.644	50.114	0.741	-0.016	0.794	2.43	0.669	0.409	0.052	0.438	0.177	0.092	3.469
129	Swaziland	Sub-Saharan Africa	4.308	0.071	4.448	4.168	9.065	0.770	50.833	0.647	-0.185	0.708	2.43	0.849	0.693	0.074	0.323	0.067	0.147	2.155
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2	Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
26	Spain	Western Europe	6.491	0.042	6.574	6.408	10.571	0.932	74.700	0.761	-0.081	0.745	2.43	1.375	1.057	0.826	0.462	0.135	0.124	2.513
55	Japan	East Asia	5.940	0.040	6.020	5.861	10.611	0.884	75.100	0.796	-0.258	0.638	2.43	1.389	0.949	0.838	0.504	0.020	0.192	2.048
76	Hong Kong S.A.R. of China	East Asia	5.477	0.049	5.573	5.380	11.000	0.836	76.820	0.717	0.067	0.403	2.43	1.525	0.841	0.893	0.408	0.232	0.342	1.236
31	Singapore	Southeast Asia	6.377	0.043	6.460	6.293	11.488	0.915	76.953	0.927	-0.018	0.082	2.43	1.695	1.019	0.897	0.664	0.176	0.547	1.379

149 rows × 20 columns

In [36]:

countries.sort_values('Healthy life expectancy',ascending = False)

Out[36]:

	Country name	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
31	Singapore	Southeast Asia	6.377	0.043	6.460	6.293	11.488	0.915	76.953	0.927	-0.018	0.082	2.43	1.695	1.019	0.897	0.664	0.176	0.547	1.379
76	Hong Kong S.A.R. of China	East Asia	5.477	0.049	5.573	5.380	11.000	0.836	76.820	0.717	0.067	0.403	2.43	1.525	0.841	0.893	0.408	0.232	0.342	1.236
55	Japan	East Asia	5.940	0.040	6.020	5.861	10.611	0.884	75.100	0.796	-0.258	0.638	2.43	1.389	0.949	0.838	0.504	0.020	0.192	2.048
26	Spain	Western Europe	6.491	0.042	6.574	6.408	10.571	0.932	74.700	0.761	-0.081	0.745	2.43	1.375	1.057	0.826	0.462	0.135	0.124	2.513
2	Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
129	Swaziland	Sub-Saharan Africa	4.308	0.071	4.448	4.168	9.065	0.770	50.833	0.647	-0.185	0.708	2.43	0.849	0.693	0.074	0.323	0.067	0.147	2.155
84	Ivory Coast	Sub-Saharan Africa	5.306	0.078	5.460	5.152	8.551	0.644	50.114	0.741	-0.016	0.794	2.43	0.669	0.409	0.052	0.438	0.177	0.092	3.469
115	Nigeria	Sub-Saharan Africa	4.759	0.052	4.861	4.658	8.533	0.740	50.102	0.737	0.037	0.878	2.43	0.663	0.625	0.051	0.433	0.212	0.039	2.736
144	Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
127	Chad	Sub-Saharan Africa	4.355	0.094	4.540	4.171	7.364	0.619	48.478	0.579	0.041	0.807	2.43	0.255	0.353	0.000	0.240	0.215	0.084	3.209

149 rows × 20 columns

In [38]:

houses = pd.read_csv('kc_house_data.csv')titanic = pd.read_csv('titanic.csv')

In [41]:

houses.sort_values(['price','bedrooms','bathrooms'], ascending = False)

Out[41]:

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	...	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
7252	6762700020	20141013T000000	7700000.0	6	8.00	12050	27600	2.5	0	3	...	13	8570	3480	1910	1987	98102	47.6298	-122.323	3940	8800
3914	9808700762	20140611T000000	7062500.0	5	4.50	10040	37325	2.0	1	2	...	11	7680	2360	1940	2001	98004	47.6500	-122.214	3930	25449
9254	9208900037	20140919T000000	6885000.0	6	7.75	9890	31374	2.0	0	4	...	13	8860	1030	2001	0	98039	47.6305	-122.240	4540	42730
4411	2470100110	20140804T000000	5570000.0	5	5.75	9200	35069	2.0	0	0	...	13	6200	3000	2001	0	98039	47.6289	-122.233	3560	24345
1448	8907500070	20150413T000000	5350000.0	5	5.00	8000	23985	2.0	0	4	...	12	6720	1280	2009	0	98004	47.6232	-122.220	4600	21750
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8274	3883800011	20141105T000000	82000.0	3	1.00	860	10426	1.0	0	0	...	6	860	0	1954	0	98146	47.4987	-122.341	1140	11250
16198	3028200080	20150324T000000	81000.0	2	1.00	730	9975	1.0	0	0	...	5	730	0	1943	0	98168	47.4808	-122.315	860	9000
465	8658300340	20140523T000000	80000.0	1	0.75	430	5050	1.0	0	0	...	4	430	0	1912	0	98014	47.6499	-121.909	1200	7500
15293	40000362	20140506T000000	78000.0	2	1.00	780	16344	1.0	0	0	...	5	780	0	1942	0	98168	47.4739	-122.280	1700	10387
1149	3421079032	20150217T000000	75000.0	1	0.00	670	43377	1.0	0	0	...	3	670	0	1966	0	98022	47.2638	-121.906	1160	42882

21613 rows × 21 columns

In [42]:

titanic.head(10)

Out[42]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
0	1	1	Allen, Miss. Elisabeth Walton	female	29	0	0	24160	211.3375	B5	S	2	?	St Louis, MO
1	1	1	Allison, Master. Hudson Trevor	male	0.9167	1	2	113781	151.55	C22 C26	S	11	?	Montreal, PQ / Chesterville, ON
2	1	0	Allison, Miss. Helen Loraine	female	2	1	2	113781	151.55	C22 C26	S	?	?	Montreal, PQ / Chesterville, ON
3	1	0	Allison, Mr. Hudson Joshua Creighton	male	30	1	2	113781	151.55	C22 C26	S	?	135	Montreal, PQ / Chesterville, ON
4	1	0	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25	1	2	113781	151.55	C22 C26	S	?	?	Montreal, PQ / Chesterville, ON
5	1	1	Anderson, Mr. Harry	male	48	0	0	19952	26.55	E12	S	3	?	New York, NY
6	1	1	Andrews, Miss. Kornelia Theodosia	female	63	1	0	13502	77.9583	D7	S	10	?	Hudson, NY
7	1	0	Andrews, Mr. Thomas Jr	male	39	0	0	112050	0	A36	S	?	?	Belfast, NI
8	1	1	Appleton, Mrs. Edward Dale (Charlotte Lamson)	female	53	2	0	11769	51.4792	C101	S	D	?	Bayside, Queens, NY
9	1	0	Artagaveytia, Mr. Ramon	male	71	0	0	PC 17609	49.5042	?	C	?	22	Montevideo, Uruguay

In [44]:

titanic.sort_values('name', key = lambda col: col.str.lower()).head(20)

Out[44]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
600	3	0	Abbing, Mr. Anthony	male	42	0	0	C.A. 5547	7.55	?	S	?	?	?
601	3	0	Abbott, Master. Eugene Joseph	male	13	0	2	C.A. 2673	20.25	?	S	?	?	East Providence, RI
602	3	0	Abbott, Mr. Rossmore Edward	male	16	1	1	C.A. 2673	20.25	?	S	?	190	East Providence, RI
603	3	1	Abbott, Mrs. Stanton (Rosa Hunt)	female	35	1	1	C.A. 2673	20.25	?	S	A	?	East Providence, RI
604	3	1	Abelseth, Miss. Karen Marie	female	16	0	0	348125	7.65	?	S	16	?	Norway Los Angeles, CA
605	3	1	Abelseth, Mr. Olaus Jorgensen	male	25	0	0	348122	7.65	F G63	S	A	?	Perkins County, SD
323	2	0	Abelson, Mr. Samuel	male	30	1	0	P/PP 3381	24	?	C	?	?	Russia New York, NY
324	2	1	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28	1	0	P/PP 3381	24	?	C	10	?	Russia New York, NY
606	3	1	Abrahamsson, Mr. Abraham August Johannes	male	20	0	0	SOTON/O2 3101284	7.925	?	S	15	?	Taalintehdas, Finland Hoboken, NJ
607	3	1	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	female	18	0	0	2657	7.2292	?	C	C	?	Greensburg, PA
608	3	0	Adahl, Mr. Mauritz Nils Martin	male	30	0	0	C 7076	7.25	?	S	?	72	Asarum, Sweden Brooklyn, NY
609	3	0	Adams, Mr. John	male	26	0	0	341826	8.05	?	S	?	103	Bournemouth, England
610	3	0	Ahlin, Mrs. Johan (Johanna Persdotter Larsson)	female	40	1	0	7546	9.475	?	S	?	?	Sweden Akeley, MN
611	3	1	Aks, Master. Philip Frank	male	0.8333	0	1	392091	9.35	?	S	11	?	London, England Norfolk, VA
612	3	1	Aks, Mrs. Sam (Leah Rosen)	female	18	0	1	392091	9.35	?	S	13	?	London, England Norfolk, VA
613	3	1	Albimona, Mr. Nassef Cassem	male	26	0	0	2699	18.7875	?	C	15	?	Syria Fredericksburg, VA
325	2	0	Aldworth, Mr. Charles Augustus	male	30	0	0	248744	13	?	S	?	?	Bryn Mawr, PA, USA
614	3	0	Alexander, Mr. William	male	26	0	0	3474	7.8875	?	S	?	?	England Albion, NY
615	3	0	Alhomaki, Mr. Ilmari Rudolf	male	20	0	0	SOTON/O2 3101287	7.925	?	S	?	?	Salo, Finland Astoria, OR
616	3	0	Ali, Mr. Ahmed	male	24	0	0	SOTON/O.Q. 3101311	7.05	?	S	?	?	?

In [45]:

countries

Out[45]:

	Country name	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
0	Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
1	Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
2	Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
3	Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
4	Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
144	Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
145	Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
146	Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
147	Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
148	Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 20 columns

In [49]:

countries.set_index('Country name').sort_index(ascending = False)

Out[49]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Zambia	Sub-Saharan Africa	4.073	0.069	4.209	3.938	8.145	0.708	55.809	0.782	0.061	0.823	2.43	0.528	0.552	0.231	0.487	0.227	0.074	1.975
Yemen	Middle East and North Africa	3.658	0.070	3.794	3.521	7.578	0.832	57.122	0.602	-0.147	0.800	2.43	0.329	0.831	0.272	0.268	0.092	0.089	1.776
Vietnam	Southeast Asia	5.411	0.039	5.488	5.334	8.973	0.850	68.034	0.940	-0.098	0.796	2.43	0.817	0.873	0.616	0.679	0.124	0.091	2.211
Venezuela	Latin America and Caribbean	4.892	0.064	5.017	4.767	9.073	0.861	66.700	0.615	-0.169	0.827	2.43	0.852	0.897	0.574	0.284	0.078	0.072	2.135
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Armenia	Commonwealth of Independent States	5.283	0.058	5.397	5.168	9.487	0.799	67.055	0.825	-0.168	0.629	2.43	0.996	0.758	0.585	0.540	0.079	0.198	2.127
Argentina	Latin America and Caribbean	5.929	0.056	6.040	5.819	9.962	0.898	69.000	0.828	-0.182	0.834	2.43	1.162	0.980	0.646	0.544	0.069	0.067	2.461
Algeria	Middle East and North Africa	4.887	0.053	4.991	4.783	9.342	0.802	66.005	0.480	-0.067	0.752	2.43	0.946	0.765	0.552	0.119	0.144	0.120	2.242
Albania	Central and Eastern Europe	5.117	0.059	5.234	5.001	9.520	0.697	68.999	0.785	-0.030	0.901	2.43	1.008	0.529	0.646	0.491	0.168	0.024	2.250
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 19 columns

In [67]:

titanic.pclass.value_counts().sort_values()

Out[67]:

2 277

1 323

3 709

Name: pclass, dtype: int64

In [69]:

titanic.pclass.value_counts().sort_index().plot(kind = 'bar')

Out[69]:

<Axes: >

In [70]:

titanic.pclass.value_counts().sort_values().plot(kind = 'bar')

Out[70]:

<Axes: >

In [76]:

houses.bedrooms.value_counts().sort_index().plot(kind = 'bar')

Out[76]:

<Axes: >

In [77]:

countries.head()

Out[77]:

	Country name	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
0	Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.0	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
1	Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.7	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
2	Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.4	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
3	Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.0	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
4	Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.4	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798

In [78]:

countries['Ladder score']

Out[78]:

0 7.842

1 7.620

2 7.571

3 7.554

4 7.464

...

144 3.512

145 3.467

146 3.415

147 3.145

148 2.523

Name: Ladder score, Length: 149, dtype: float64

In [88]:

df = countries.set_index('Country name')df

Out[88]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 19 columns

In [96]:

df.loc[['India','Canada','United Kingdom','United States']]

Out[96]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
India	South Asia	3.819	0.026	3.869	3.769	8.755	0.603	60.633	0.893	0.089	0.774	2.43	0.741	0.316	0.383	0.622	0.246	0.106	1.405
Canada	North America and ANZ	7.103	0.042	7.185	7.021	10.776	0.926	73.800	0.915	0.089	0.415	2.43	1.447	1.044	0.798	0.648	0.246	0.335	2.585
United Kingdom	Western Europe	7.064	0.038	7.138	6.990	10.707	0.934	72.500	0.859	0.233	0.459	2.43	1.423	1.062	0.757	0.580	0.340	0.306	2.596
United States	North America and ANZ	6.951	0.049	7.047	6.856	11.023	0.920	68.200	0.837	0.098	0.698	2.43	1.533	1.030	0.621	0.554	0.252	0.154	2.807

In [97]:

titanic.loc[[8,9,896]]

Out[97]:

pclass

survived

name

sex

age

sibsp

parch

ticket

fare

cabin

embarked

boat

body

home.dest

Appleton, Mrs. Edward Dale (Charlotte Lamson)

female

11769

51.4792

C101

Bayside, Queens, NY

Artagaveytia, Mr. Ramon

male

PC 17609

49.5042

Montevideo, Uruguay

896

Johnson, Mr. Alfred

male

LINE

In [99]:

titanic.loc[5:10]

Out[99]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
5	1	1	Anderson, Mr. Harry	male	48	0	0	19952	26.55	E12	S	3	?	New York, NY
6	1	1	Andrews, Miss. Kornelia Theodosia	female	63	1	0	13502	77.9583	D7	S	10	?	Hudson, NY
7	1	0	Andrews, Mr. Thomas Jr	male	39	0	0	112050	0	A36	S	?	?	Belfast, NI
8	1	1	Appleton, Mrs. Edward Dale (Charlotte Lamson)	female	53	2	0	11769	51.4792	C101	S	D	?	Bayside, Queens, NY
9	1	0	Artagaveytia, Mr. Ramon	male	71	0	0	PC 17609	49.5042	?	C	?	22	Montevideo, Uruguay
10	1	0	Astor, Col. John Jacob	male	47	1	0	PC 17757	227.525	C62 C64	C	?	124	New York, NY

In [100]:

titanic.loc[1:100:2]

Out[100]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
1	1	1	Allison, Master. Hudson Trevor	male	0.9167	1	2	113781	151.55	C22 C26	S	11	?	Montreal, PQ / Chesterville, ON
3	1	0	Allison, Mr. Hudson Joshua Creighton	male	30	1	2	113781	151.55	C22 C26	S	?	135	Montreal, PQ / Chesterville, ON
5	1	1	Anderson, Mr. Harry	male	48	0	0	19952	26.55	E12	S	3	?	New York, NY
7	1	0	Andrews, Mr. Thomas Jr	male	39	0	0	112050	0	A36	S	?	?	Belfast, NI
9	1	0	Artagaveytia, Mr. Ramon	male	71	0	0	PC 17609	49.5042	?	C	?	22	Montevideo, Uruguay
11	1	1	Astor, Mrs. John Jacob (Madeleine Talmadge Force)	female	18	1	0	PC 17757	227.525	C62 C64	C	4	?	New York, NY
13	1	1	Barber, Miss. Ellen 'Nellie'	female	26	0	0	19877	78.85	?	S	6	?	?
15	1	0	Baumann, Mr. John D	male	?	0	0	PC 17318	25.925	?	S	?	?	New York, NY
17	1	1	Baxter, Mrs. James (Helene DeLaudeniere Chaput)	female	50	0	1	PC 17558	247.5208	B58 B60	C	6	?	Montreal, PQ
19	1	0	Beattie, Mr. Thomson	male	36	0	0	13050	75.2417	C6	C	A	?	Winnipeg, MN
21	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47	1	1	11751	52.5542	D35	S	5	?	New York, NY
23	1	1	Bidois, Miss. Rosalie	female	42	0	0	PC 17757	227.525	?	C	4	?	?
25	1	0	Birnbaum, Mr. Jakob	male	25	0	0	13905	26	?	C	?	148	San Francisco, CA
27	1	1	Bishop, Mrs. Dickinson H (Helen Walton)	female	19	1	0	11967	91.0792	B49	C	7	?	Dowagiac, MI
29	1	1	Bjornstrom-Steffansson, Mr. Mauritz Hakan	male	28	0	0	110564	26.55	C52	S	D	?	Stockholm, Sweden / Washington, DC
31	1	1	Blank, Mr. Henry	male	40	0	0	112277	31	A31	C	7	?	Glen Ridge, NJ
33	1	1	Bonnell, Miss. Elizabeth	female	58	0	0	113783	26.55	C103	S	8	?	Birkdale, England Cleveland, Ohio
35	1	1	Bowen, Miss. Grace Scott	female	45	0	0	PC 17608	262.375	?	C	4	?	Cooperstown, NY
37	1	1	Bradley, Mr. George ('George Arthur Brayton')	male	?	0	0	111427	26.55	?	S	9	?	Los Angeles, CA
39	1	0	Brandeis, Mr. Emil	male	48	0	0	PC 17591	50.4958	B10	C	?	208	Omaha, NE
41	1	1	Brown, Mrs. James Joseph (Margaret Tobin)	female	44	0	0	PC 17610	27.7208	B4	C	6	?	Denver, CO
43	1	1	Bucknell, Mrs. William Robert (Emma Eliza Ward)	female	60	0	0	11813	76.2917	D15	C	8	?	Philadelphia, PA
45	1	0	Butt, Major. Archibald Willingham	male	45	0	0	113050	26.55	B38	S	?	?	Washington, DC
47	1	1	Calderhead, Mr. Edward Pennington	male	42	0	0	PC 17476	26.2875	E24	S	5	?	New York, NY
49	1	1	Cardeza, Mr. Thomas Drake Martinez	male	36	0	1	PC 17755	512.3292	B51 B53 B55	C	3	?	Austria-Hungary / Germantown, Philadelphia, PA
51	1	0	Carlsson, Mr. Frans Olof	male	33	0	0	695	5	B51 B53 B55	S	?	?	New York, NY
53	1	0	Carrau, Mr. Jose Pedro	male	17	0	0	113059	47.1	?	S	?	?	Montevideo, Uruguay
55	1	1	Carter, Miss. Lucile Polk	female	14	1	2	113760	120	B96 B98	S	4	?	Bryn Mawr, PA
57	1	1	Carter, Mrs. William Ernest (Lucile Polk)	female	36	1	2	113760	120	B96 B98	S	4	?	Bryn Mawr, PA
59	1	1	Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genev...	female	?	0	0	17770	27.7208	?	C	5	?	New York, NY
61	1	1	Cavendish, Mrs. Tyrell William (Julia Florence...	female	76	1	0	19877	78.85	C46	S	6	?	Little Onn Hall, Staffs
63	1	1	Chaffee, Mrs. Herbert Fuller (Carrie Constance...	female	47	1	0	W.E.P. 5734	61.175	E31	S	4	?	Amenia, ND
65	1	1	Chambers, Mrs. Norman Campbell (Bertha Griggs)	female	33	1	0	113806	53.1	E8	S	5	?	New York, NY / Ithaca, NY
67	1	1	Cherry, Miss. Gladys	female	30	0	0	110152	86.5	B77	S	8	?	London, England
69	1	1	Chibnall, Mrs. (Edith Martha Bowerman)	female	?	0	1	113505	55	E33	S	6	?	St Leonards-on-Sea, England Ohio
71	1	0	Clark, Mr. Walter Miller	male	27	1	0	13508	136.7792	C89	C	?	?	Los Angeles, CA
73	1	1	Cleaver, Miss. Alice	female	22	0	0	113781	151.55	?	S	11	?	?
75	1	0	Colley, Mr. Edward Pomeroy	male	47	0	0	5727	25.5875	E58	S	?	?	Victoria, BC
77	1	0	Compton, Mr. Alexander Taylor Jr	male	37	1	1	PC 17756	83.1583	E52	C	?	?	Lakewood, NJ
79	1	1	Cornell, Mrs. Robert Clifford (Malvina Helen L...	female	55	2	0	11770	25.7	C101	S	2	?	New York, NY
81	1	0	Crosby, Capt. Edward Gifford	male	70	1	1	WE/P 5735	71	B22	S	?	269	Milwaukee, WI
83	1	1	Crosby, Mrs. Edward Gifford (Catherine Elizabe...	female	64	1	1	112901	26.55	B26	S	7	?	Milwaukee, WI
85	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38	1	0	PC 17599	71.2833	C85	C	4	?	New York, NY
87	1	1	Daniel, Mr. Robert Williams	male	27	0	0	113804	30.5	?	S	3	?	Philadelphia, PA
89	1	0	Davidson, Mr. Thornton	male	31	1	0	F.C. 12750	52	B71	S	?	?	Montreal, PQ
91	1	1	Dick, Mr. Albert Adrian	male	31	1	0	17474	57	B20	S	3	?	Calgary, AB
93	1	1	Dodge, Dr. Washington	male	53	1	1	33638	81.8583	A34	S	13	?	San Francisco, CA
95	1	1	Dodge, Mrs. Washington (Ruth Vidaver)	female	54	1	1	33638	81.8583	A34	S	5	?	San Francisco, CA
97	1	1	Douglas, Mrs. Frederick Charles (Mary Helene B...	female	27	1	1	PC 17558	247.5208	B58 B60	C	6	?	Montreal, PQ
99	1	1	Duff Gordon, Lady. (Lucille Christiana Sutherl...	female	48	1	0	11755	39.6	A16	C	1	?	London / Paris

In [107]:

df.sort_index().loc['Denmark':'France':2]

Out[107]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Ecuador	Latin America and Caribbean	5.764	0.057	5.875	5.653	9.313	0.821	68.800	0.842	-0.124	0.843	2.43	0.935	0.806	0.640	0.560	0.107	0.062	2.653
El Salvador	Latin America and Caribbean	6.061	0.065	6.188	5.933	9.054	0.762	66.402	0.888	-0.110	0.688	2.43	0.845	0.675	0.565	0.615	0.116	0.160	3.085
Ethiopia	Sub-Saharan Africa	4.275	0.051	4.374	4.175	7.694	0.764	59.000	0.752	0.082	0.761	2.43	0.370	0.679	0.331	0.451	0.241	0.114	2.089
France	Western Europe	6.690	0.037	6.762	6.618	10.704	0.942	74.000	0.822	-0.147	0.571	2.43	1.421	1.081	0.804	0.536	0.092	0.235	2.521

In [112]:

df.iloc[20:100:2]

Out[112]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
France	Western Europe	6.690	0.037	6.762	6.618	10.704	0.942	74.000	0.822	-0.147	0.571	2.43	1.421	1.081	0.804	0.536	0.092	0.235	2.521
Malta	Western Europe	6.602	0.044	6.688	6.516	10.674	0.931	72.200	0.927	0.133	0.653	2.43	1.411	1.055	0.747	0.664	0.275	0.183	2.268
United Arab Emirates	Middle East and North Africa	6.561	0.039	6.637	6.484	11.085	0.844	67.333	0.932	0.074	0.589	2.43	1.555	0.860	0.594	0.670	0.236	0.223	2.422
Spain	Western Europe	6.491	0.042	6.574	6.408	10.571	0.932	74.700	0.761	-0.081	0.745	2.43	1.375	1.057	0.826	0.462	0.135	0.124	2.513
Slovenia	Central and Eastern Europe	6.461	0.043	6.546	6.376	10.529	0.948	71.400	0.949	-0.101	0.806	2.43	1.360	1.093	0.722	0.690	0.122	0.085	2.388
Uruguay	Latin America and Caribbean	6.431	0.046	6.521	6.341	9.966	0.925	69.100	0.896	-0.092	0.590	2.43	1.164	1.042	0.649	0.625	0.128	0.223	2.600
Kosovo	Central and Eastern Europe	6.372	0.059	6.487	6.257	9.318	0.821	63.813	0.869	0.257	0.917	2.43	0.937	0.807	0.483	0.593	0.356	0.014	3.182
Brazil	Latin America and Caribbean	6.330	0.043	6.415	6.245	9.577	0.882	66.601	0.804	-0.071	0.756	2.43	1.028	0.944	0.571	0.514	0.142	0.117	3.015
Jamaica	Latin America and Caribbean	6.309	0.156	6.615	6.004	9.186	0.877	67.500	0.890	-0.137	0.884	2.43	0.891	0.932	0.599	0.618	0.099	0.035	3.135
Cyprus	Western Europe	6.223	0.049	6.319	6.128	10.576	0.802	73.898	0.763	-0.015	0.844	2.43	1.377	0.765	0.801	0.464	0.178	0.061	2.578
Panama	Latin America and Caribbean	6.180	0.073	6.323	6.036	10.350	0.896	69.652	0.872	-0.166	0.856	2.43	1.298	0.976	0.667	0.596	0.079	0.053	2.509
Chile	Latin America and Caribbean	6.172	0.046	6.262	6.081	10.071	0.882	70.000	0.742	-0.044	0.830	2.43	1.200	0.946	0.678	0.438	0.159	0.070	2.682
Kazakhstan	Commonwealth of Independent States	6.152	0.047	6.243	6.060	10.155	0.952	65.200	0.853	-0.069	0.733	2.43	1.230	1.103	0.527	0.573	0.143	0.132	2.446
Kuwait	Middle East and North Africa	6.106	0.066	6.235	5.977	10.817	0.843	66.900	0.867	-0.104	0.736	2.43	1.461	0.857	0.580	0.591	0.120	0.130	2.368
El Salvador	Latin America and Caribbean	6.061	0.065	6.188	5.933	9.054	0.762	66.402	0.888	-0.110	0.688	2.43	0.845	0.675	0.565	0.615	0.116	0.160	3.085
Latvia	Central and Eastern Europe	6.032	0.036	6.103	5.961	10.315	0.927	67.100	0.715	-0.162	0.800	2.43	1.285	1.047	0.587	0.405	0.082	0.089	2.536
Hungary	Central and Eastern Europe	5.992	0.047	6.085	5.899	10.358	0.943	68.000	0.755	-0.186	0.876	2.43	1.301	1.083	0.615	0.454	0.067	0.040	2.432
Nicaragua	Latin America and Caribbean	5.972	0.083	6.134	5.810	8.620	0.864	67.657	0.836	0.020	0.664	2.43	0.693	0.904	0.604	0.553	0.201	0.176	2.841
Argentina	Latin America and Caribbean	5.929	0.056	6.040	5.819	9.962	0.898	69.000	0.828	-0.182	0.834	2.43	1.162	0.980	0.646	0.544	0.069	0.067	2.461
Honduras	Latin America and Caribbean	5.919	0.082	6.081	5.758	8.648	0.812	67.300	0.857	0.081	0.809	2.43	0.703	0.787	0.593	0.578	0.241	0.083	2.934
Philippines	Southeast Asia	5.880	0.052	5.982	5.778	9.076	0.830	62.000	0.917	-0.097	0.742	2.43	0.853	0.828	0.426	0.651	0.125	0.126	2.872
Peru	Latin America and Caribbean	5.840	0.075	5.988	5.692	9.458	0.832	68.250	0.822	-0.154	0.891	2.43	0.986	0.833	0.623	0.536	0.087	0.031	2.744
Moldova	Commonwealth of Independent States	5.766	0.046	5.856	5.677	9.454	0.857	65.699	0.822	-0.079	0.918	2.43	0.985	0.888	0.542	0.536	0.137	0.013	2.665
Kyrgyzstan	Commonwealth of Independent States	5.744	0.046	5.834	5.653	8.538	0.893	64.401	0.935	0.119	0.908	2.43	0.665	0.971	0.501	0.673	0.266	0.020	2.648
Bolivia	Latin America and Caribbean	5.716	0.053	5.819	5.613	9.046	0.810	63.901	0.875	-0.077	0.839	2.43	0.842	0.782	0.486	0.600	0.138	0.064	2.805
Paraguay	Latin America and Caribbean	5.653	0.092	5.832	5.473	9.448	0.893	65.900	0.876	0.028	0.882	2.43	0.983	0.970	0.549	0.602	0.206	0.037	2.306
Dominican Republic	Latin America and Caribbean	5.545	0.071	5.685	5.405	9.802	0.853	66.102	0.860	-0.133	0.714	2.43	1.106	0.879	0.555	0.581	0.101	0.144	2.178
Belarus	Commonwealth of Independent States	5.534	0.047	5.625	5.442	9.853	0.910	66.253	0.650	-0.180	0.627	2.43	1.124	1.007	0.560	0.326	0.070	0.199	2.247
Hong Kong S.A.R. of China	East Asia	5.477	0.049	5.573	5.380	11.000	0.836	76.820	0.717	0.067	0.403	2.43	1.525	0.841	0.893	0.408	0.232	0.342	1.236
Vietnam	Southeast Asia	5.411	0.039	5.488	5.334	8.973	0.850	68.034	0.940	-0.098	0.796	2.43	0.817	0.873	0.616	0.679	0.124	0.091	2.211
Malaysia	Southeast Asia	5.384	0.049	5.480	5.289	10.238	0.817	67.102	0.895	0.125	0.839	2.43	1.259	0.797	0.587	0.624	0.270	0.064	1.784
Congo (Brazzaville)	Sub-Saharan Africa	5.342	0.097	5.533	5.151	8.117	0.636	58.221	0.695	-0.068	0.745	2.43	0.518	0.392	0.307	0.381	0.144	0.124	3.476
Ivory Coast	Sub-Saharan Africa	5.306	0.078	5.460	5.152	8.551	0.644	50.114	0.741	-0.016	0.794	2.43	0.669	0.409	0.052	0.438	0.177	0.092	3.469
Nepal	South Asia	5.269	0.070	5.406	5.132	8.120	0.774	64.233	0.782	0.152	0.727	2.43	0.519	0.702	0.496	0.488	0.287	0.135	2.642
Maldives	South Asia	5.198	0.072	5.339	5.057	9.826	0.913	70.600	0.854	0.024	0.825	2.43	1.115	1.015	0.697	0.575	0.204	0.073	1.520
Cameroon	Sub-Saharan Africa	5.142	0.074	5.288	4.996	8.189	0.710	53.515	0.731	0.026	0.848	2.43	0.543	0.556	0.159	0.425	0.205	0.058	3.195
Albania	Central and Eastern Europe	5.117	0.059	5.234	5.001	9.520	0.697	68.999	0.785	-0.030	0.901	2.43	1.008	0.529	0.646	0.491	0.168	0.024	2.250
Ghana	Sub-Saharan Africa	5.088	0.067	5.219	4.958	8.580	0.727	57.586	0.807	0.123	0.848	2.43	0.680	0.595	0.287	0.517	0.268	0.058	2.684
Turkmenistan	Commonwealth of Independent States	5.066	0.036	5.136	4.996	9.629	0.983	62.409	0.877	0.273	0.888	2.43	1.046	1.172	0.439	0.602	0.366	0.033	1.409
Benin	Sub-Saharan Africa	5.045	0.073	5.189	4.901	8.087	0.489	54.713	0.757	-0.034	0.661	2.43	0.507	0.058	0.196	0.457	0.166	0.178	3.482

In [120]:

house = houses.sort_index(ascending = False)house

Out[120]:

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	...	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
21612	1523300157	20141015T000000	325000.0	2	0.75	1020	1076	2.0	0	0	...	7	1020	0	2008	0	98144	47.5941	-122.299	1020	1357
21611	291310100	20150116T000000	400000.0	3	2.50	1600	2388	2.0	0	0	...	8	1600	0	2004	0	98027	47.5345	-122.069	1410	1287
21610	1523300141	20140623T000000	402101.0	2	0.75	1020	1350	2.0	0	0	...	7	1020	0	2009	0	98144	47.5944	-122.299	1020	2007
21609	6600060120	20150223T000000	400000.0	4	2.50	2310	5813	2.0	0	0	...	8	2310	0	2014	0	98146	47.5107	-122.362	1830	7200
21608	263000018	20140521T000000	360000.0	3	2.50	1530	1131	3.0	0	0	...	8	1530	0	2009	0	98103	47.6993	-122.346	1530	1509
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4	1954400510	20150218T000000	510000.0	3	2.00	1680	8080	1.0	0	0	...	8	1680	0	1987	0	98074	47.6168	-122.045	1800	7503
3	2487200875	20141209T000000	604000.0	4	3.00	1960	5000	1.0	0	0	...	7	1050	910	1965	0	98136	47.5208	-122.393	1360	5000
2	5631500400	20150225T000000	180000.0	2	1.00	770	10000	1.0	0	0	...	6	770	0	1933	0	98028	47.7379	-122.233	2720	8062
1	6414100192	20141209T000000	538000.0	3	2.25	2570	7242	2.0	0	0	...	7	2170	400	1951	1991	98125	47.7210	-122.319	1690	7639
0	7129300520	20141013T000000	221900.0	3	1.00	1180	5650	1.0	0	0	...	7	1180	0	1955	0	98178	47.5112	-122.257	1340	5650

21613 rows × 21 columns

In [126]:

house.loc[21612:21611, ['price', 'bedrooms']]

Out[126]:

	price	bedrooms
21612	325000.0	2
21611	400000.0	3

In [129]:

titanic.loc[50:60:2, ['name', 'sex', 'age']]

Out[129]:

	name	sex	age
50	Cardeza, Mrs. James Warburton Martinez (Charlo...	female	58
52	Carrau, Mr. Francisco M	male	28
54	Carter, Master. William Thornton II	male	11
56	Carter, Mr. William Ernest	male	36
58	Case, Mr. Howard Brown	male	49
60	Cavendish, Mr. Tyrell William	male	36

In [132]:

df.loc['Denmark':'Canada', 'Ladder score']

Out[132]:

Country name

Denmark 7.620

Switzerland 7.571

Iceland 7.554

Netherlands 7.464

Norway 7.392

Sweden 7.363

Luxembourg 7.324

New Zealand 7.277

Austria 7.268

Australia 7.183

Israel 7.157

Germany 7.155

Canada 7.103

Name: Ladder score, dtype: float64

In [139]:

houses = pd.read_csv('kc_house_data.csv')houses

Out[139]:

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	...	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
0	7129300520	20141013T000000	221900.0	3	1.00	1180	5650	1.0	0	0	...	7	1180	0	1955	0	98178	47.5112	-122.257	1340	5650
1	6414100192	20141209T000000	538000.0	3	2.25	2570	7242	2.0	0	0	...	7	2170	400	1951	1991	98125	47.7210	-122.319	1690	7639
2	5631500400	20150225T000000	180000.0	2	1.00	770	10000	1.0	0	0	...	6	770	0	1933	0	98028	47.7379	-122.233	2720	8062
3	2487200875	20141209T000000	604000.0	4	3.00	1960	5000	1.0	0	0	...	7	1050	910	1965	0	98136	47.5208	-122.393	1360	5000
4	1954400510	20150218T000000	510000.0	3	2.00	1680	8080	1.0	0	0	...	8	1680	0	1987	0	98074	47.6168	-122.045	1800	7503
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
21608	263000018	20140521T000000	360000.0	3	2.50	1530	1131	3.0	0	0	...	8	1530	0	2009	0	98103	47.6993	-122.346	1530	1509
21609	6600060120	20150223T000000	400000.0	4	2.50	2310	5813	2.0	0	0	...	8	2310	0	2014	0	98146	47.5107	-122.362	1830	7200
21610	1523300141	20140623T000000	402101.0	2	0.75	1020	1350	2.0	0	0	...	7	1020	0	2009	0	98144	47.5944	-122.299	1020	2007
21611	291310100	20150116T000000	400000.0	3	2.50	1600	2388	2.0	0	0	...	8	1600	0	2004	0	98027	47.5345	-122.069	1410	1287
21612	1523300157	20141015T000000	325000.0	2	0.75	1020	1076	2.0	0	0	...	7	1020	0	2008	0	98144	47.5941	-122.299	1020	1357

21613 rows × 21 columns

In [143]:

houses['bedrooms'].value_counts().loc[33]

Out[143]:

In [145]:

titanic.info()

RangeIndex: 1309 entries, 0 to 1308

Data columns (total 14 columns):

# Column Non-Null Count Dtype

--- ------ -------------- -----

0 pclass 1309 non-null int64

1 survived 1309 non-null int64

2 name 1309 non-null object

3 sex 1309 non-null object

4 age 1309 non-null object

5 sibsp 1309 non-null int64

6 parch 1309 non-null int64

7 ticket 1309 non-null object

8 fare 1309 non-null object

9 cabin 1309 non-null object

10 embarked 1309 non-null object

11 boat 1309 non-null object

12 body 1309 non-null object

13 home.dest 1309 non-null object

dtypes: int64(4), object(10)

memory usage: 143.3+ KB

In [151]:

titanic['age'].value_counts().iloc[0:5]

Out[151]:

? 263

24 47

22 43

21 41

30 40

Name: age, dtype: int64

In [152]:

pokemon = pd.read_csv('pokemon.csv')pokemon

Out[152]:

	Num	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	2	False
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	3	False
3	4	Charmander	Fire	NaN	309	39	52	43	60	50	65	1	False
4	5	Charmeleon	Fire	NaN	405	58	64	58	80	65	80	2	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
146	147	Dratini	Dragon	NaN	300	41	64	45	50	50	50	1	False
147	148	Dragonair	Dragon	NaN	420	61	84	65	70	70	70	2	False
148	149	Dragonite	Dragon	Flying	600	91	134	95	100	100	80	3	False
149	150	Mewtwo	Psychic	NaN	680	106	110	90	154	90	130	1	True
150	151	Mew	Psychic	NaN	600	100	100	100	100	100	100	1	False

151 rows × 13 columns

In [153]:

pokemon.set_index('Name')

Out[153]:

	Num	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
Name
Bulbasaur	1	Grass	Poison	318	45	49	49	65	65	45	1	False
Ivysaur	2	Grass	Poison	405	60	62	63	80	80	60	2	False
Venusaur	3	Grass	Poison	525	80	82	83	100	100	80	3	False
Charmander	4	Fire	NaN	309	39	52	43	60	50	65	1	False
Charmeleon	5	Fire	NaN	405	58	64	58	80	65	80	2	False
...	...	...	...	...	...	...	...	...	...	...	...	...
Dratini	147	Dragon	NaN	300	41	64	45	50	50	50	1	False
Dragonair	148	Dragon	NaN	420	61	84	65	70	70	70	2	False
Dragonite	149	Dragon	Flying	600	91	134	95	100	100	80	3	False
Mewtwo	150	Psychic	NaN	680	106	110	90	154	90	130	1	True
Mew	151	Psychic	NaN	600	100	100	100	100	100	100	1	False

151 rows × 12 columns

In [158]:

pokemon.sort_values('Attack', ascending = False)

Out[158]:

	Num	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
148	149	Dragonite	Dragon	Flying	600	91	134	95	100	100	80	3	False
111	112	Rhydon	Ground	Rock	485	105	130	120	45	45	40	2	False
135	136	Flareon	Fire	NaN	525	65	130	60	95	110	65	2	False
67	68	Machamp	Fighting	NaN	505	90	130	80	65	85	55	3	False
98	99	Kingler	Water	NaN	475	55	130	115	50	50	75	2	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
13	14	Kakuna	Bug	Poison	205	45	25	50	25	25	35	2	False
62	63	Abra	Psychic	NaN	310	25	20	15	105	55	90	1	False
10	11	Metapod	Bug	NaN	205	50	20	55	25	25	30	2	False
128	129	Magikarp	Water	NaN	200	20	10	55	15	20	80	1	False
112	113	Chansey	Normal	NaN	450	250	5	5	35	105	50	1	False

151 rows × 13 columns

In [164]:

pokemon.sort_values(['Total','Attack','Sp. Atk'], ascending = False)

Out[164]:

	Num	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
149	150	Mewtwo	Psychic	NaN	680	106	110	90	154	90	130	1	True
148	149	Dragonite	Dragon	Flying	600	91	134	95	100	100	80	3	False
150	151	Mew	Psychic	NaN	600	100	100	100	100	100	100	1	False
145	146	Moltres	Fire	Flying	580	90	100	90	125	85	90	1	True
144	145	Zapdos	Electric	Flying	580	90	90	85	125	90	100	1	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...
13	14	Kakuna	Bug	Poison	205	45	25	50	25	25	35	2	False
10	11	Metapod	Bug	NaN	205	50	20	55	25	25	30	2	False
128	129	Magikarp	Water	NaN	200	20	10	55	15	20	80	1	False
12	13	Weedle	Bug	Poison	195	40	35	30	20	20	50	1	False
9	10	Caterpie	Bug	NaN	195	45	30	35	20	20	45	1	False

151 rows × 13 columns

In [169]:

pokemon.set_index('Name').sort_index().tail(20)

Out[169]:

	Num	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
Name
Staryu	120	Water	NaN	340	30	45	55	70	55	85	1	False
Tangela	114	Grass	NaN	435	65	55	115	100	40	60	1	False
Tauros	128	Normal	NaN	490	75	100	95	40	70	110	1	False
Tentacool	72	Water	Poison	335	40	40	35	50	100	70	1	False
Tentacruel	73	Water	Poison	515	80	70	65	80	120	100	2	False
Vaporeon	134	Water	NaN	525	130	65	60	110	95	65	2	False
Venomoth	49	Bug	Poison	450	70	65	60	90	75	90	2	False
Venonat	48	Bug	Poison	305	60	55	50	40	55	45	1	False
Venusaur	3	Grass	Poison	525	80	82	83	100	100	80	3	False
Victreebel	71	Grass	Poison	490	80	105	65	100	70	70	3	False
Vileplume	45	Grass	Poison	490	75	80	85	110	90	50	3	False
Voltorb	100	Electric	NaN	330	40	30	50	55	55	100	1	False
Vulpix	37	Fire	NaN	299	38	41	40	50	65	65	1	False
Wartortle	8	Water	NaN	405	59	63	80	65	80	58	2	False
Weedle	13	Bug	Poison	195	40	35	30	20	20	50	1	False
Weepinbell	70	Grass	Poison	390	65	90	50	85	45	55	2	False
Weezing	110	Poison	NaN	490	65	90	120	85	70	60	2	False
Wigglytuff	40	Normal	Fairy	435	140	70	45	85	50	45	2	False
Zapdos	145	Electric	Flying	580	90	90	85	125	90	100	1	True
Zubat	41	Poison	Flying	245	40	45	35	30	40	55	1	False

In [172]:

pok_df = pokemon.sort_values('Speed',ascending = False)pok_df

Out[172]:

	Num	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
100	101	Electrode	Electric	NaN	480	60	50	70	80	80	140	2	False
141	142	Aerodactyl	Rock	Flying	515	80	105	65	60	75	130	1	False
134	135	Jolteon	Electric	NaN	525	65	65	60	110	95	130	2	False
149	150	Mewtwo	Psychic	NaN	680	106	110	90	154	90	130	1	True
64	65	Alakazam	Psychic	NaN	500	55	50	45	135	95	120	3	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
45	46	Paras	Bug	Grass	285	35	70	55	45	55	25	1	False
110	111	Rhyhorn	Ground	Rock	345	80	85	95	30	30	25	1	False
38	39	Jigglypuff	Normal	Fairy	270	115	45	20	45	25	20	1	False
73	74	Geodude	Rock	Ground	300	40	80	100	30	30	20	1	False
78	79	Slowpoke	Water	Psychic	315	90	65	65	40	40	15	1	False

151 rows × 13 columns

In [181]:

pok_df.head(10).describe()

Out[181]:

	Num	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage
count	10.000000	10.000000	10.000000	10.00000	10.000000	10.000000	10.000000	10.000000	10.000000
mean	97.200000	504.000000	66.100000	79.50000	67.500000	88.400000	80.500000	122.000000	1.800000
std	44.656218	72.141836	18.465884	21.53163	17.199806	36.999099	10.658851	10.055402	0.632456
min	26.000000	405.000000	35.000000	50.00000	45.000000	40.000000	65.000000	110.000000	1.000000
25%	56.000000	481.250000	60.000000	66.25000	56.250000	61.250000	71.250000	115.000000	1.250000
50%	111.000000	495.000000	62.500000	77.50000	62.500000	85.000000	80.000000	120.000000	2.000000
75%	133.250000	518.750000	72.500000	97.50000	81.250000	107.500000	88.750000	130.000000	2.000000
max	150.000000	680.000000	106.000000	110.00000	95.000000	154.000000	95.000000	140.000000	3.000000

In [182]:

pok_df.head(20).describe()

Out[182]:

	Num	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage
count	20.000000	20.00000	20.000000	20.000000	20.000000	20.000000	20.000000	20.000000	20.00000
mean	87.900000	498.55000	68.100000	80.200000	67.600000	87.800000	82.300000	112.550000	1.85000
std	44.750654	61.17832	16.861354	20.962623	17.135374	30.453761	14.382372	12.141599	0.67082
min	18.000000	400.00000	35.000000	35.000000	30.000000	40.000000	61.000000	100.000000	1.00000
25%	52.500000	479.75000	60.000000	68.750000	59.250000	64.000000	70.000000	104.000000	1.00000
50%	86.000000	500.00000	65.000000	80.000000	65.000000	80.500000	80.000000	110.000000	2.00000
75%	125.750000	515.00000	76.250000	100.000000	76.250000	102.500000	91.250000	120.000000	2.00000
max	151.000000	680.00000	106.000000	110.000000	100.000000	154.000000	120.000000	140.000000	3.00000

In [183]:

pok_df.value_counts('Type 1')

Out[183]:

Type 1

Water 28

Normal 22

Poison 14

Bug 12

Fire 12

Grass 12

Electric 9

Rock 9

Ground 8

Psychic 8

Fighting 7

Dragon 3

Ghost 3

Fairy 2

Ice 2

dtype: int64

In [187]:

pokemon = pd.read_csv('pokemon.csv')pokemon

Out[187]:

	Num	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	2	False
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	3	False
3	4	Charmander	Fire	NaN	309	39	52	43	60	50	65	1	False
4	5	Charmeleon	Fire	NaN	405	58	64	58	80	65	80	2	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
146	147	Dratini	Dragon	NaN	300	41	64	45	50	50	50	1	False
147	148	Dragonair	Dragon	NaN	420	61	84	65	70	70	70	2	False
148	149	Dragonite	Dragon	Flying	600	91	134	95	100	100	80	3	False
149	150	Mewtwo	Psychic	NaN	680	106	110	90	154	90	130	1	True
150	151	Mew	Psychic	NaN	600	100	100	100	100	100	100	1	False

151 rows × 13 columns

In [189]:

pd = pokemon.set_index('Name')pd

Out[189]:

	Num	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
Name
Bulbasaur	1	Grass	Poison	318	45	49	49	65	65	45	1	False
Ivysaur	2	Grass	Poison	405	60	62	63	80	80	60	2	False
Venusaur	3	Grass	Poison	525	80	82	83	100	100	80	3	False
Charmander	4	Fire	NaN	309	39	52	43	60	50	65	1	False
Charmeleon	5	Fire	NaN	405	58	64	58	80	65	80	2	False
...	...	...	...	...	...	...	...	...	...	...	...	...
Dratini	147	Dragon	NaN	300	41	64	45	50	50	50	1	False
Dragonair	148	Dragon	NaN	420	61	84	65	70	70	70	2	False
Dragonite	149	Dragon	Flying	600	91	134	95	100	100	80	3	False
Mewtwo	150	Psychic	NaN	680	106	110	90	154	90	130	1	True
Mew	151	Psychic	NaN	600	100	100	100	100	100	100	1	False

151 rows × 12 columns

In [190]:

pd.loc['Diglett']

Out[190]:

Num 50

Type 1 Ground

Type 2 NaN

Total 265

HP 10

Attack 55

Defense 25

Sp. Atk 35

Sp. Def 45

Speed 95

Stage 1

Legendary False

Name: Diglett, dtype: object

In [202]:

pd.loc[['Eevee','Vulpix','Dragonair']]

Out[202]:

	Num	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
Name
Eevee	133	Normal	NaN	325	55	55	50	45	65	55	1	False
Vulpix	37	Fire	NaN	299	38	41	40	50	65	65	1	False
Dragonair	148	Dragon	NaN	420	61	84	65	70	70	70	2	False

In [205]:

pd.sort_index().loc['Charizard':'Charmeleon']

Out[205]:

	Num	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
Name
Charizard	6	Fire	Flying	534	78	84	78	109	85	100	3	False
Charmander	4	Fire	NaN	309	39	52	43	60	50	65	1	False
Charmeleon	5	Fire	NaN	405	58	64	58	80	65	80	2	False

In [207]:

pd.sort_index().iloc[[30,40,50]]

Out[207]:

	Num	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
Name
Eevee	133	Normal	NaN	325	55	55	50	45	65	55	1	False
Gengar	94	Ghost	Poison	500	60	65	60	130	75	110	3	False
Gyarados	130	Water	Flying	540	95	125	79	60	100	81	2	False

In [208]:

fish_pokemon = ['Magikarp','Goldeen','Horsea','Seaking','Seadra','Gyarados']pd.sort_index().loc[fish_pokemon]

Out[208]:

	Num	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
Name
Magikarp	129	Water	NaN	200	20	10	55	15	20	80	1	False
Goldeen	118	Water	NaN	320	45	67	60	35	50	63	1	False
Horsea	116	Water	NaN	295	30	40	70	70	25	60	1	False
Seaking	119	Water	NaN	450	80	92	65	65	80	68	2	False
Seadra	117	Water	NaN	440	55	65	95	95	45	85	2	False
Gyarados	130	Water	Flying	540	95	125	79	60	100	81	2	False

In [213]:

water = pd.sort_index().loc[fish_pokemon]df = water.sort_values('Attack')

In [31]:

Out[31]:

	Num	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	2	False
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	3	False
3	4	Charmander	Fire	NaN	309	39	52	43	60	50	65	1	False
4	5	Charmeleon	Fire	NaN	405	58	64	58	80	65	80	2	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
146	147	Dratini	Dragon	NaN	300	41	64	45	50	50	50	1	False
147	148	Dragonair	Dragon	NaN	420	61	84	65	70	70	70	2	False
148	149	Dragonite	Dragon	Flying	600	91	134	95	100	100	80	3	False
149	150	Mewtwo	Psychic	NaN	680	106	110	90	154	90	130	1	True
150	151	Mew	Psychic	NaN	600	100	100	100	100	100	100	1	False

151 rows × 13 columns

In [32]:

pok_attack = df[['Attack']]pok_attack

Out[32]:

	Attack
0	49
1	62
2	82
3	52
4	64
...	...
146	64
147	84
148	134
149	110
150	100

151 rows × 1 columns

In [235]:

pok_attack.plot(kind = 'bar', color = 'purple')

Out[235]:

<Axes: xlabel='Name'>

In [17]:

import pandas as pd

In [21]:

df = pd.read_csv('pokemon.csv')df

Out[21]:

	Num	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	2	False
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	3	False
3	4	Charmander	Fire	NaN	309	39	52	43	60	50	65	1	False
4	5	Charmeleon	Fire	NaN	405	58	64	58	80	65	80	2	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
146	147	Dratini	Dragon	NaN	300	41	64	45	50	50	50	1	False
147	148	Dragonair	Dragon	NaN	420	61	84	65	70	70	70	2	False
148	149	Dragonite	Dragon	Flying	600	91	134	95	100	100	80	3	False
149	150	Mewtwo	Psychic	NaN	680	106	110	90	154	90	130	1	True
150	151	Mew	Psychic	NaN	600	100	100	100	100	100	100	1	False

151 rows × 13 columns

In [25]:

pok_att = df.set_index('Name').sort_values('Attack',ascending = False).head(10)pok_att

Out[25]:

	Num	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
Name
Dragonite	149	Dragon	Flying	600	91	134	95	100	100	80	3	False
Rhydon	112	Ground	Rock	485	105	130	120	45	45	40	2	False
Flareon	136	Fire	NaN	525	65	130	60	95	110	65	2	False
Machamp	68	Fighting	NaN	505	90	130	80	65	85	55	3	False
Kingler	99	Water	NaN	475	55	130	115	50	50	75	2	False
Pinsir	127	Bug	NaN	500	65	125	100	55	70	85	1	False
Gyarados	130	Water	Flying	540	95	125	79	60	100	81	2	False
Hitmonlee	106	Fighting	NaN	455	50	120	53	35	110	87	1	False
Golem	76	Rock	Ground	495	80	120	130	55	65	45	3	False
Kabutops	141	Rock	Water	495	60	115	105	65	70	80	2	False

In [30]:

pok_att[['Attack']].describe()

Out[30]:

	Attack
count	10.000000
mean	125.900000
std	5.989806
min	115.000000
25%	121.250000
50%	127.500000
75%	130.000000
max	134.000000

In [ ]: